Classification of Personnel Targets with Baggage Using Dual-band Radar

Abstract

Abstract:In this paper, we aim to identify passengers with different baggage by analyzing the micro-Doppler radar signatures corresponding to different kinds of gaits, which is helpful to improve the efficiency of security check in airports. After performing time-frequency analysis on the X-band and K-band radar data, three kinds of micro-Doppler features, i.e., the period, the Doppler offset, and the bandwidth, are extracted from the time-frequency domain. By combining the features extracted by dual-band radar with the one-versus-one support vector machine (SVM) classifier, three kinds of gaits, i.e., walking with no bag, walking with only one carry-on baggage by one hand, and walking with one carry-on baggage by one hand and one handbag by another hand, can be accurately classified. The experimental results based on the measured data demonstrate that the classification accuracy using dual-band radar is higher than that using only a single-band radar sensor.

Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

“…Target classification using micro-Doppler signatures has seen a rapid growth in recent years [2][3][4][5][6], with application in fields including surveillance [2] [3], healthcare [4] [5] and human-computer interaction [6]. Based on the human micro-Doppler signature, personnel recognition and human activity classification have attracted much attention [5,[7][8][9][10]. In [7], empirical features with clear physical meaning are used to train a support vector machine (SVM) classifier.…”

“…In [7], empirical features with clear physical meaning are used to train a support vector machine (SVM) classifier. Similar classification tools are used in [8] on dual frequency radar micro-Doppler signatures. The authors of [9] propose some features based on singular value decomposition (SVD) of the spectrogram, which yield good performance in classification of unarmed/armed personnel outdoors.…”

Sparsity aware dynamic gesture recognition using radar sensors with angular diversity

“…multiple sensors observe the same target from different aspects at the same time. As demonstrated in [20–23], a proper fusion of the data collected by multiple sensors with angular diversity may effectively improve the recognition accuracy in comparison with processing on single sensor data.…”

Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD

“…In radar jargon, the micro-Doppler effect is the phenomenon of additional sidebands appearing around the central Doppler spectra of the target, which is highly related to its kinematics and geometric features [7]. Micro-motion and micro-Doppler phenomena can be observed everywhere in our lives and contribute to the fields of radar-based exploration and surveillance of humanity activities [8]. Its underlying civilian and military applications include security monitoring, urban warfare, law enforcement, healthcare, kinematics, search/rescue, anti-terrorism surveillance, and so on [9,10].…”